Fair Performance Metric Elicitation
Not on the map. Why?
Abstract (arXiv)
What is a fair performance metric? We consider the choice of fairness metrics through the lens of metric elicitation -- a principled framework for selecting performance metrics that best reflect implicit preferences. The use of metric elicitation enables a practitioner to tune the performance and fairness metrics to the task, context, and population at hand. Specifically, we propose a novel strategy to elicit group-fair performance metrics for multiclass classification problems with multiple sensitive groups that also includes selecting the trade-off between predictive performance and fairness violation. The proposed elicitation strategy requires only relative preference feedback and is robust to both finite sample and feedback noise.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jun 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 2; 2023: 3; 2024: 1; 2025: 3; 2026: 1.
13 papers.
Sort:
By area:
- 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
- 2023Moral Machine or Tyranny of the Majority?cited by 22
- 2021Uncertain Decisions Facilitate Better Preference Learningcited by 10
- 2022Perspectives on Incorporating Expert Feedback into Model Updatescited by 7
- 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
- 2021Optimizing Black-box Metrics with Iterative Example Weightingcited by 6
- 2023Superhuman Fairnesscited by 1
- 2024A Note on Bias to Completecited by 1
- 2022Metric Elicitation; Moving from Theory to Practicecited by 0
- 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
- 2025An Efficient Plugin Method for Metric Optimization of Black-Box Modelscited by 0
- 2025"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessmentcited by 0
- 2026How Much Imprecision is Enough Imprecision in my Classifier? A Practical Elicitation Procedure27 Sep 2026
- 2025"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment22 Sep 2025
- 2025An Efficient Plugin Method for Metric Optimization of Black-Box Models3 Mar 2025
- 2025A Critical Review of Predominant Bias in Neural Networks16 Feb 2025
- 2024A Note on Bias to Complete18 Feb 2024
- 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools29 Sep 2023
- 2023Moral Machine or Tyranny of the Majority?27 May 2023
- 2023Superhuman Fairness31 Jan 2023
- 2022Metric Elicitation; Moving from Theory to Practice7 Dec 2022
- 2022Perspectives on Incorporating Expert Feedback into Model Updates13 May 2022
- 2021A Sociotechnical View of Algorithmic Fairness27 Sep 2021
- 2021Uncertain Decisions Facilitate Better Preference Learning19 Jun 2021
1 more
- 2021Uncertain Decisions Facilitate Better Preference Learningcited by 10
- 2021Uncertain Decisions Facilitate Better Preference Learning19 Jun 2021
- 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
- 2023Moral Machine or Tyranny of the Majority?cited by 22
- 2022Perspectives on Incorporating Expert Feedback into Model Updatescited by 7
- 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
- 2021Optimizing Black-box Metrics with Iterative Example Weightingcited by 6
- 2023Superhuman Fairnesscited by 1
- 2024A Note on Bias to Completecited by 1
- 2022Metric Elicitation; Moving from Theory to Practicecited by 0
- 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
- 2025An Efficient Plugin Method for Metric Optimization of Black-Box Modelscited by 0
- 2025"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessmentcited by 0
- 2026How Much Imprecision is Enough Imprecision in my Classifier? A Practical Elicitation Procedurecited by 0
- 2026How Much Imprecision is Enough Imprecision in my Classifier? A Practical Elicitation Procedure27 Sep 2026
- 2025"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment22 Sep 2025
- 2025An Efficient Plugin Method for Metric Optimization of Black-Box Models3 Mar 2025
- 2025A Critical Review of Predominant Bias in Neural Networks16 Feb 2025
- 2024A Note on Bias to Complete18 Feb 2024
- 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools29 Sep 2023
- 2023Moral Machine or Tyranny of the Majority?27 May 2023
- 2023Superhuman Fairness31 Jan 2023
- 2022Metric Elicitation; Moving from Theory to Practice7 Dec 2022
- 2022Perspectives on Incorporating Expert Feedback into Model Updates13 May 2022
- 2021A Sociotechnical View of Algorithmic Fairness27 Sep 2021
- 2021Optimizing Black-box Metrics with Iterative Example Weighting18 Feb 2021